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Scala for Machine Learning

You're reading from   Scala for Machine Learning Leverage Scala and Machine Learning to construct and study systems that can learn from data

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783558742
Length 624 pages
Edition 1st Edition
Languages
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Author (1):
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Patrick R. Nicolas Patrick R. Nicolas
Author Profile Icon Patrick R. Nicolas
Patrick R. Nicolas
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Table of Contents (15) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Hello World! 3. Data Preprocessing 4. Unsupervised Learning 5. Naïve Bayes Classifiers 6. Regression and Regularization 7. Sequential Data Models 8. Kernel Models and Support Vector Machines 9. Artificial Neural Networks 10. Genetic Algorithms 11. Reinforcement Learning 12. Scalable Frameworks A. Basic Concepts Index

Apache Spark


Apache Spark is a fast and general-purpose cluster computing system, initially developed as AMPLab/UC Berkeley as part of the Berkeley Data Analytics Stack (BDAS) (http://en.wikipedia.org/wiki/UC_Berkeley). It provides high-level APIs for the following programming languages that make large and concurrent parallel jobs easy to write and deploy [12:11]:

Note

The link to the latest information

The URLs as any reference to Apache Spark may change in future versions.

The core element of Spark is a resilient distributed dataset (RDD), which is a collection of elements partitioned across the nodes of a cluster and/or CPU cores of servers. An RDD can be created from a local data structure such as a list, array, or hash table, from the local filesystem or the Hadoop distributed file system (HDFS...

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